A prospective cohort of hospitalized patients with anti-MDA5 positive DM-ILD was setup since April 2014 in our center. All patients initially fulfilled Bohan and Peter’s criteria for DM or Sontheimer’s criteria for clinically amyopathic dermatomyositis on admission (14, 15), were re-evaluated retrospectively and considered eligible as long as they also met the recent 239th ENMC classification criteria for DM (16). All patients were with imaging-confirmed ILD and positive anti-MDA5 antibody. The anti-MDA5 antibody was detected by immunoblotting assay (Euroimmun, Germany) and confirmed by ELISA (Supplementary Data S1). ILD course was defined as time from the first abnormal pulmonary CT which revealed ILD changes to admission. Patients with ILD course > 3 months or with coexisting malignancy (within 3 years) or with pre-existing chronic obstructive pulmonary disease were excluded. The primary outcome was the six-month all-cause mortality since the time of admission.
A total of 173 eligible patients were enrolled and were further divided into two datasets. Patients admitted between April 2014 and December 2018 (n=116) versus those admitted between January 2019 and January 2020 (n=57), were defined as the derivation dataset and the validation dataset, respectively (Figure 1).
Clinical data including age, gender, physical findings, respiratory function, treatment history and outcomes were obtained from medical records. The study protocol was approved by the ethics committees of our hospital and the need to obtain informed consent was waived.
HRCT images acquisition and visual scoring
Patients underwent non-contrast pulmonary HRCT at the day around admission (median, 2 days; range, 1–6 days), using multidetector CT scanner (United Imaging, Shanghai, China; Siemens Healthineers, Forchheim, Germany). CT slice thickness was 1.0–1.5mm at 10mm intervals in the whole lungs.
All CT images were reviewed by two observers (YZ with 10-years’ experience and CZ with 5-years’ experience in chest HRCT imaging evaluation) who were blinded to patients’ outcome. Inter-observer variability was evaluated by Intraclass correlation coefficient (ICC). The results were agreed upon by consensus between the two observers.
For the previously reported IPF-based visual scoring method (‘IPF score’), HRCT findings were evaluated for GGO, consolidation, TBE and honeycombing defined by the Fleischner Society, and were graded on a scale of 1-6 based on the classification system (8). The overall ‘IPF score’ was calculated by summing the average score of six zones (upper, middle, and lower on both sides) as described; and was used as a comparator for the following analysis.
Three components, i.e. GGO, consolidation and fibrosis, were separately rated and recorded according to pulmonary involvement area of the five lobes (right upper, right middle, right lower, left upper and left lower lobes of the lung). The 0–5 scoring for GGO or consolidation at each lobe was adopted (0, no involvement; 1, ≤5% involvement; 2, 5 to <25% involvement; 3, 25-49% involvement; 4, 50-75% involvement; 5, >75% involvement). Similarly, the fibrotic change in each lobe was classified into 5 grades (0, no fibrosis; 1, interlobular septal thickening without honeycombing; 2, honeycombing < 25%; 3, 25-49%; 4, 50-75%; 5, > 75% of the lobe) as fibrosis score (9, 10). The respective total score of each component (GGO, consolidation and fibrosis) was the sum of each lobe’s score and ranged from 0 (no involvement) to 25 (maximum involvement).
AI algorithm-based CT quantitative analysis
The Digital Imaging and Communications in Medicine files of CT images were inputted and run on a software package named “CT Pneumonia Analysis” (syngo.via Frontier 1.0, Siemens Healthineers, Forchheim, Germany). The algorithm had been first trained on a large cohort of patients with various diseases, then fine-tuned with a cohort with abnormal patterns including GGO, consolidation, effusions, and masses, to improve the robustness of the lung segmentation over the involved areas. Based on 3D segmentations of lesions, lungs, and lobes, the AI algorithm automatically detected and quantified abnormal tomographic patterns commonly present in pneumonia, such as GGO and consolidation both globally and lobe-wise.
The percentage of total opacity (total lesions) as well as the percentage of consolidation (with a cutoff of CT value≥-200 Hounsfield unit) was directly calculated for the whole lung. Then by subtracting consolidation from total lesion, the percentage of GGO was obtained for further analysis.
Clinical data were described and compared between the derivation and validation datasets by univariable analysis. The Mann-Whitney U test, Chi-square test and Fisher's exact test were conducted, as appropriate. Clinical features with >5% missing data were excluded for analysis.
Among the three visual scoring components, i.e. GGO, consolidation and fibrosis, variables significantly associated with outcome in the univariable analysis were subsequently included in the multivariable COX proportional hazards model. The derived β regression coefficients were used to construct a linear weighted scoring model, defined as ‘MDA5 score’. Likewise, the percentage of GGO and consolidation from AI algorithm based quantitative analysis were used to construct another weighted scoring model, defined as ‘AI score’.
The optimal cutoff value of CT score was identified by receiver operating characteristic curve analysis. The association between CT score and six-month survival were assessed by Kaplan-Meier survival plot and log-rank test.
Model discrimination of the ‘IPF score’, ‘MDA5 score’ and ‘AI score’ models were quantified and compared by the Harrell concordance index (C-index) with 95% confidence interval (CI). A decision curve analysis (DCA) was built to determine and compare the clinical usefulness of each model(17). Significance was defined as p<0.05.
Statistical analyses were performed by SPSS software version 25 (IBM Corp., Armonk, NY, USA), and R software version 3.6.1 (http://www.Rproject.org). All the R codes were available at Github (https://github.com/tomato08217/MDA5).